[unreadable] [unreadable] This proposed study will build upon the extensive epidemiologic database and specimen repository derived from an ongoing bladder cancer study entitled "Genetic Susceptibility to Bladder Cancer: A Molecular Epidemiologic Approach" (RO1 CA74880, PI: Xifeng Wu). The parent grant involved a multidisciplinary group of researchers applying a molecular epidemiologic approach to identify inter-individual differences in susceptibility to bladder carcinogenesis, with a focus on genes involved in protecting against genomic instability, such as DNA repair, cell cycle, and telomere maintenance. It is increasing recognized that inflammation plays an important role in the etiology of many cancers, including bladder cancer (BC). We have recently shown that an IL-6 promoter SNP (G-174C) is associated with BC risk and progression. In this application, we will test the hypothesis that BC is modulated by common, low penetrance polymorphisms in inflammation related genes, interacting with each other and/or environmental factors. We will take a pathway based genotyping approach and apply novel statistical tools to address the hypothesis. There are 3 specific aims: Specific Aim 1: To identify candidate polymorphisms in inflammation related genes that predispose individuals to BC. We plan to perform genotyping of a comprehensive list of potential functional polymorphisms in inflammation genes (85 polymorphisms in 44 genes) on 1000 Caucasian cases and 1000 controls matched on age, gender, and ethnicity. Specific Aim 2: To perform genotyping on tagging SNPs for the top 8 candidate genes that were identified in Specific Aim 1 (using a significance level of 0.1) identified in Specific Aim 1. This will give us a complete picture of the role that the specific gene plays in BC etiology. We will implement haplotype-based analyses to identify any additional variants that might have been missed using individual SNP-based approaches. Specific Aim 3: To apply hierarchical models to refine risk assessment and to apply novel machine-learning tools to identify any gene-environment and gene- gene interactions influencing risk for BC. These analyses will examine SNP main effects and gene-gene interactions, gene-environment interaction and develop and validate algorithms, which will identify individuals at highest risk for BC, given their personal exposure patterns and their genetic risk profiles. The application will allow us to have a comprehensive picture of the effects of functional polymorphisms in inflammation related genes on BC risk. [unreadable] [unreadable] [unreadable]